3 Search Results for "Grech, Neville"

Document
Artifact
DOI: 10.4230/DARTS.6.2.6
Static Analysis of Shape in TensorFlow Programs (Artifact)

Authors: Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, and Yannis Smaragdakis

Published in: DARTS, Volume 6, Issue 2, Special Issue of the 34th European Conference on Object-Oriented Programming (ECOOP 2020)

Abstract
These instructions are intended for using the artifact for our ECOOP'20 paper entitled "Static Analysis of Shape in TensorFlow Programs". They can be used to run Pythia - the tool implementing the paper’s analysis - on the paper’s evaluation set demonstrating bug detection in the most precise configuration of our analysis as well as the precision of the analysis under different configurations.
Cite as

Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, and Yannis Smaragdakis. Static Analysis of Shape in TensorFlow Programs (Artifact). In Special Issue of the 34th European Conference on Object-Oriented Programming (ECOOP 2020). Dagstuhl Artifacts Series (DARTS), Volume 6, Issue 2, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{lagouvardos_et_al:DARTS.6.2.6,
  author =	{Lagouvardos, Sifis and Dolby, Julian and Grech, Neville and Antoniadis, Anastasios and Smaragdakis, Yannis},
  title =	{{Static Analysis of Shape in TensorFlow Programs (Artifact)}},
  pages =	{6:1--6:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2020},
  volume =	{6},
  number =	{2},
  editor =	{Lagouvardos, Sifis and Dolby, Julian and Grech, Neville and Antoniadis, Anastasios and Smaragdakis, Yannis},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DARTS.6.2.6},
  URN =		{urn:nbn:de:0030-drops-132035},
  doi =		{10.4230/DARTS.6.2.6},
  annote =	{Keywords: Python, TensorFlow, static analysis, Doop, Wala}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2020.15
Static Analysis of Shape in TensorFlow Programs

Authors: Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, and Yannis Smaragdakis

Published in: LIPIcs, Volume 166, 34th European Conference on Object-Oriented Programming (ECOOP 2020)

Abstract
Machine learning has been widely adopted in diverse science and engineering domains, aided by reusable libraries and quick development patterns. The TensorFlow library is probably the best-known representative of this trend and most users employ the Python API to its powerful back-end. TensorFlow programs are susceptible to several systematic errors, especially in the dynamic typing setting of Python. We present Pythia, a static analysis that tracks the shapes of tensors across Python library calls and warns of several possible mismatches. The key technical aspects are a close modeling of library semantics with respect to tensor shape, and an identification of violations and error-prone patterns. Pythia is powerful enough to statically detect (with 84.62% precision) 11 of the 14 shape-related TensorFlow bugs in the recent Zhang et al. empirical study - an independent slice of real-world bugs.
Cite as

Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, and Yannis Smaragdakis. Static Analysis of Shape in TensorFlow Programs. In 34th European Conference on Object-Oriented Programming (ECOOP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 166, pp. 15:1-15:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{lagouvardos_et_al:LIPIcs.ECOOP.2020.15,
  author =	{Lagouvardos, Sifis and Dolby, Julian and Grech, Neville and Antoniadis, Anastasios and Smaragdakis, Yannis},
  title =	{{Static Analysis of Shape in TensorFlow Programs}},
  booktitle =	{34th European Conference on Object-Oriented Programming (ECOOP 2020)},
  pages =	{15:1--15:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-154-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{166},
  editor =	{Hirschfeld, Robert and Pape, Tobias},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2020.15},
  URN =		{urn:nbn:de:0030-drops-131726},
  doi =		{10.4230/LIPIcs.ECOOP.2020.15},
  annote =	{Keywords: Python, TensorFlow, static analysis, Doop, Wala}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2018.26
Efficient Reflection String Analysis via Graph Coloring

Authors: Neville Grech, George Kastrinis, and Yannis Smaragdakis

Published in: LIPIcs, Volume 109, 32nd European Conference on Object-Oriented Programming (ECOOP 2018)

Abstract
Static analyses for reflection and other dynamic language features have recently increased in number and advanced in sophistication. Most such analyses rely on a whole-program model of the flow of strings, through the stack and heap. We show that this global modeling of strings remains a major bottleneck of static analyses and propose a compact encoding, in order to battle unnecessary complexity. In our encoding, strings are maximally merged if they can never serve to differentiate class members in reflection operations. We formulate the problem as an instance of graph coloring and propose a fast polynomial-time algorithm that exploits the unique features of the setting (esp. large cliques, leading to hundreds of colors for realistic programs). The encoding is applied to two different frameworks for string-guided Java reflection analysis from past literature and leads to significant optimization (e.g., a ~2x reduction in the number of string-flow inferences), for a whole-program points-to analysis that uses strings.
Cite as

Neville Grech, George Kastrinis, and Yannis Smaragdakis. Efficient Reflection String Analysis via Graph Coloring. In 32nd European Conference on Object-Oriented Programming (ECOOP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 109, pp. 26:1-26:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{grech_et_al:LIPIcs.ECOOP.2018.26,
  author =	{Grech, Neville and Kastrinis, George and Smaragdakis, Yannis},
  title =	{{Efficient Reflection String Analysis via Graph Coloring}},
  booktitle =	{32nd European Conference on Object-Oriented Programming (ECOOP 2018)},
  pages =	{26:1--26:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-079-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{109},
  editor =	{Millstein, Todd},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2018.26},
  URN =		{urn:nbn:de:0030-drops-92319},
  doi =		{10.4230/LIPIcs.ECOOP.2018.26},
  annote =	{Keywords: reflection, static analysis, graph coloring}
}
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